Large-scale analytics platforms can be expensive to build and maintain. As such, it is advantageous and often necessary to run such platforms at high rates of utilization. Also, such platforms are commonly required to run different kinds of jobs on a given cluster, wherein such jobs might include, for example, production jobs with deadlines, batch jobs, low latency, interactive jobs, service jobs, etc. Large-scale platforms are also commonly used by a large number of users, and such users often create significant overlapping computations among the submitted jobs, which can present opportunities that can be leveraged, for example, with respect to meeting service level agreement (SLA) obligations.